Stereoscopic displays allow presenting an image that is perceived by a user as a three-dimensional (3D) image. To this end, a stereoscopic display directs information from certain sub-pixels of an image in different directions, so that a viewer can see a different picture with each eye. If the pictures are similar enough, the human brain will assume that the viewer is looking at a single object and fuse matching points on the two pictures together to create a perceived single object. The human brain will match similar nearby points from the left and right eye input. Small horizontal differences in the location of points will be represented as disparity, allowing the eye to converge to the point, and building a perception of the depth of every object in the scene relative to the disparity perceived between the eyes. This enables the brain to fuse the pictures into a single perceived 3D object.
The data for a 3D image may be obtained for instance by taking multiple two-dimensional images and by combining the pixels of the images to sub-pixels of a single image for the presentation on a stereoscopic display.
In one alternative, two cameras that are arranged at a small pre-specified distance relative to each other take the two-dimensional images for a 3D presentation.
Each camera usually comprises an image chain applying an image processing to the captured images, as known from conventional two-dimensional (2D) image processing.
A Euclidian image shift with image edge cropping is applied to the processed images to move the zero displacement plane or zero disparity plane (ZDP) to lie in the middle of the virtual scene, in order to converge the images. The images are then combined by interlacing to form a 3D presentation.
In the context of the ZDP, disparity is a horizontal linear measure of the difference between where a point is represented on a left hand image and where it is represented on a right hand image. There are different measures for this disparity, for example arc-min of the eye, diopter limits, maximum disparity on the display, distance out of the display at which an object is placed, etc. These measures are all geometrically related to each other, though, so determining the disparity with one measure defines it as well for any other measure for a certain viewing geometry. When taking two pictures with parallel cameras, the cameras pick up a zero angular disparity between them for an object at infinite distance, and a maximum angular disparity for a close object, that is, a maximum number of pixels disparity, which depends on the closeness of the object and the camera separation, as well as on other factors, like camera resolution, field of view (FOV), zoom and lens properties. Therefore the horizontal disparity between two input images taken by two parallel cameras ranges from zero to maximum disparity. On the display side, there is a certain viewing geometry defining for instance an allowed diopter mismatch, relating to a maximum convergence angle and thus to a maximum disparity on the screen.
The image cropping removes the non-overlapping parts of the images, and due to the Euclidian image shift, the remaining pixels of both images in the ZDP have the same indices. In the ZDP, all points in a XY plane lie on the same position on both left and right images, causing the effect of objects to be perceived in the plane of the screen. The ZDP is normally adjusted to be near the middle of the virtual scene and represents the depth of objects that appear on the depth of the screen. Objects with positive disparity appear in front of the screen and objects with negative disparity appear behind the screen. The horizontal Euclidian shift moves the ZDP and respectively changes all the object disparities relative to it, hence moving the scene in its entirety forwards or backwards in the comfortable virtual viewing space (CVVS).
On the display side, the disparity may range from a negative maximum value for an object that appears at a back limit plane (BLP) and a maximum positive value for an object that appears at a frontal limit plane (FLP). FLP and BLP thus provide limits in the virtual space as to how far a virtual object may appear in front of the screen or behind the screen.
Since in stereoscopic imaging the left and right images are simultaneously viewed and compared to each other, the slightest of differences between the images are readily detected. This makes stereoscopic imaging much more susceptible to minute discrepancies between the images which would not even be noticed in standard 2D presentations. With the human brain making the assumption that both images are derived from one 3D object, it compares the images, looking for slight differences. For example, an artifact causing an object offset of one pixel would not be noticeable in a 2D imaging, but in stereoscopy it could represent the difference between an object being at infinite distance and being at 100 m. Also differences in gamma, contrast, color balance and/or sharpness etc. become readily detectable, and can cause severe negative nauseating sensations. For example, while incorrect exposure of up to one f-stop is not critical in a 2D photography, such a difference in a stereo image pair can cause varying degrees of eye strain and/or nausea in poorly created content, producing a degradation of the stereoscopic viewing experience.
The images directed to the left and right eye should thus ideally not have any differences except the parallax arising due to a different perspective point.
For creating high quality 3D images, the alignment of the employed cameras is critical. Further, both cameras should have the same aperture, that is, the same diameter of the iris. It is also very desirable that both cameras use the same focus depth. However, focus can be very sensitive to control and due to manufacturing discrepancies it may not be directly possible to clone the focus of one camera to the other. Similarly, the sensitivities of employed charge-coupled device (CCD) matrices may vary and thus cloning the gain values applied by these matrices may not result in similar images. In addition, there may also be other types of mismatching between the images due to different camera properties, for example a mismatch of white balance, sharpness, granularity and various other image factors.